Energy Consumption, Economic Growth And Environmental Sustainability Challenges For Belt And Road Countries: A Fresh Insight From “Chinese Going Global Strategy”

The present study investigated impact of energy and economy related variables on CO 2 25 emissions in 49 countries of belt and road initiative from 1995-2018. The robust type of cross- 26 section dependence and heterogeneity methods were adopted to analyze data set of countries. 27 Energy consumption, foreign direct investment, medium and high-tech industry, and GDP has 28 been found highly unfavorable for the ecological health (CO 2 emissions) in 49 nations on BRI 29 panel. However, renewable energy consumption has been found in positive correlation with 30 environmental quality (CO 2 ). Financial development indicator has no significant impact on CO 2 31 emissions in present study. The present outcomes clearly claim strong relationship of economic 32 growth and energy with increased CO 2 emissions in 49 nations. Therefore, it is important for 33 policy makers, experts and governments to incentivize and appreciate portfolio investors for 34 sustainable green investments to transform the economic growth into a sustainable and energy 35 efficient development.


Introduction
In the current era of development and modernization, climate change is the biggest threat  The energy growth has strong correlation with financial development (here in the sense 97 of economic growth) and environmental change which can be found in literature. As, Grossman 98 and Krueger (Grossman &Krueger 1995) testified the three stages of Environmental Kuznet 99 curve (EKC) (Kuznets 1955) where first phase focusses on evolution of economy along with   Table 1A).

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The present study used CO2 as dependent variable and other variables mentioned in Table   146 1 considered independent variables. The dataset has been log-transformed for the purpose of 147 standardization. This standardization will minimize the robustness from data and will minimize 148 the enlargement of coefficients, multi-correlations and autocorrelations related problems. The 149 description and sources of all the variables have been stated in Table 1. 159 Here in Eq. 1, Carbon dioxide as mentioned earlier is the dependent variable and 160 relationship will be estimated as "CO2 is equal to the function of independent variables". The  CO 2 i,t = α + β 1 lnECON i,t + β 2 lnFD i,t + β 3 lnGDP i,t + β 4 lnFDI i,t + β 5 lnMHI i,t + β 6 lnTOP i,t 172 + β 7 lnREC i,t + ε i,t Therefore, both the CD and LM test are structured in the following ways: Whereas, ̂2 is residuals correlation, which was valued by using Ordinary Least Square 190 equation. The results of the above given equations are given below in Table 2  The cross dependence of the dynamic panels for residuals of dataset has been 195 investigated by using CD tests of Frees (Frees 1995(Frees , 2004) Friedman (1937) and Pesaran (2004).

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The short term and large cross-sectional residual dependence for given dataset (time and number  The results (Table 2 and Table 3) show the cross dependence in the dataset. Therefore,

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we need to apply 2 nd type of CD tests to justify the hitch of cross-dependence. Pesaran (Pesaran 222 2007) defined the process of cross-sectional Im, Pesaran, and Shin (CIPS) and cross-sectional 223 augmented Dickey-Fuller (CADF). The country to county cross-sectional dependence, reliability 224 and steadfastness will be the outcomes of these two methods with their natural heterogeneity.

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Therefore, the test may further be built as follows: Whereas; = constant, ̅ = mean of cross-section at "t" period, and = lag operator.

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The results of both types of unit root tests approved the stability of dataset. Co-234 integration tests by Pedroni (1999), (Pedroni 2004 for alternative hypothesis. The co-integration among variables can be seen in Table 9. The 249 uniformity between the target variables has been found normally distributed, which verifies the 250 Pedroni co-integration test. This relationship could be written as following equation; In equation 9, and V stand for the Monte Carlo oriented adjustment measures.

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The first four results of Panels (v, rho, PP, and ADF statistics) in the Table 9 are within-254 dimension statistics and latter three Groups (rho, PP and ADF) are between the dimension 255 statistics. Therefore, we have at least 4 statistics out of 7 fulfills the lowest criterion for long-run 256 linear co-integration approval within target variables.

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After the approval of cross dependence between the target variables, the co-integration  Table 8. 262 The results show that co-integration exists among CO2 and all other independent variables for all 263 the 49 countries of the study regions. study to explore the long-run co-integration amongst variables.

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Meanwhile, the following FMOLS and DOLS equations are presented to test the hypotheses: (10)

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Where And Ω i = Ω i 0 + Γ i + Γ′ i  results obtained through current investigation can help the policy makers to achieve "Green BRI" 284 goals in regional panels. The summary of statistics has been presented in Table 4

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The present study correlation analyses results show the highly significant positive

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This study used first-generation/type LLC, IPS and ADF ( Westerlund co-integration test under the cross-dependence situation proved to be the best choice 331 as shown in Table 9 which validates the long-run co-integration between variables. The results of 332 Pedroni (Table 10) and Kao (Table 11) shows the high level of co-integration among all 333 variables in long-run. Further investigations using FMOLS and DOLS models will give insight 334 into the long-run co-integration in full and regional panels.      392 The causality between CO2 and independent variables has been investigated using

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The results of DOLS and FMOLS presented to see robustness ( The transfer of technology between the partner countries can also help to transform the economic 466 growth into an energy efficient and sustainable development.

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The estimates of recent study suggest some essential policy implications for lawmakers 468 and environmental experts. They must allocate economic resources based on the results of the 469 study to maximize productivity, but wisely. As a result, researchers will take short-and long- for developing renewable energy supply strategies to avoid the risk of (GHG) emissions not only 476 for the BRI partnered nations but it will be great gadget for larger countries of the world. It is 477 also important to anticipate demand and supply of energy to achieve the development of BRI 478 projects. In addition, improved GDP per capita (income) will allow the general public with the 479 provision of more dynamic and environment friendly services. Therefore, it is also important for 480 policy makers to incentivize and appreciate investors for green investment and inform them 481 about its benefits.

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Additionally, researchers can modify variables that may produce points that can further 483 help to improve the understanding about the impacts of BRI projects investments on 484 Environment in general and on the regional climate in particular. In addition, we could measure   Investments of China in BRI countries from 2013-H12020 (million USD) 1 Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.